Digital image processing is the use of computer algorithms to perform image processing on digital images. Digital images may include an array of image pixel values. Image processing may involve image enlargement, deblurring of the digital image, removing noise and artifacts from the digital image, as well as encoding the pixel values during image compression. In image processing, point prediction or estimating pixel values in digital images may be used.
Currently, there are numerous processes for estimating pixel values in digital images. Some of these processes are based on image statistics and use first and second order statistics, or are based on estimates of the parameters of specific statistical models. Other processes are not based on statistics, but upon a deterministic model of images that may involve estimation of the model parameters. For example, some processes involve estimation of pixel values by fitting smooth functions and some other processes locate edges in an image and make estimates based on the location of these edges. All of these processes may use overly restrictive assumptions about the underlying structure of images, which may lead to a reduction in accuracy. Further, many of these processes are computationally intensive and processes that rely on fitting are limited in the size of the set of training signals that can be used to specify the estimator.
By using these methods that are restrictive in their measurements and assumptions about the underlying structure of the image, the process for estimating image pixel values given a digitized array of image pixel values may not be as accurate or efficient as possible. If, however, algorithms could be developed without these restrictive measurements and assumptions, and limitations on the size of the training set, then accuracy and computational efficiency for estimating pixel values may be improved.
Therefore, there is a need in the art for improving the accuracy and computational efficiency in estimating image pixel values in a digitized array of image pixel values by developing algorithms without these restrictions in measurements and assumptions. The present invention addresses these and other needs in the art.